Open AccessModel Prediksi Kelulusan Mahasiswa Menggunakan Decision Tree C4.5 dan Software Weka
Author(s) -
Isnan Mulia,
Muanas Muanas
Publication year - 2021
Publication title -
jurnal analisis sistem pendidikan tinggi indonesia
Language(s) - English
Resource type - Journals
eISSN - 2620-5718
pISSN - 2580-5339
DOI - 10.36339/jaspt.v5i1.417
Subject(s) - graduation (instrument) , decision tree , computer science , machine learning , decision tree learning , statistics , data mining , mathematics , geometry
In this research, we build a model to predict graduation status of students in Institut Bisnis dan Informatika Kesatuan using C4.5 decision tree algorithm. The prediction model is built using students’ GPA from semester 1 to semester 4, for students with admission year of 2013 to 2016. The prediction model obtained is a decision tree with 26 rules, with the attribute IPS_4 being the attribute that determines the graduation label of students. This prediction model yields an accuracy of 73%, a result that is not good enough. This result is probably due to unbalanced proportion of the data used.